CPS: Medium: An AI-enabled Cyber-Physical-Biological System for Cardiac Organoid Maturation

CPS：中：用于心脏类器官成熟的人工智能网络物理生物系统

• 批准号: 2038603
• 负责人: Jia Liu
• 金额: $ 89.82万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038603&HistoricalAwards=false
• 关键词: CPS; Medium; AI; enabled; Cyber

The ability to determine and control the maturation of human-induced pluripotent stem cell (hiPSC) derived tissues is critical to tissue engineering, regenerative medicine, pharmacology, and synthetic biology, which requires the interrogation and intervention of cellular activities across the three-dimensional (3D) volume of tissues and over the time course of tissue development at cellular resolution. This proposal aims to build an AI-enabled cyber-physical-biological system to monitor and control the maturation of hiPSC derived cardiomyocyte (hiPSC-CM) organoids during development. The proposed research will develop “tissue-like” nanoelectronics that can be integrated into the developing cardiac organoids, distributing the electronic sensor and actuator network throughout the entire 3D volume of the tissue and enabling tissue-level recording and control over the entire time course of development at single-cell resolution. In situ single-cell RNA sequencing will be used to integrate gene expression data with continuous physical sensing data. Machine learning and statistical models will be built for interpreting the online sensing data, and cyber-control methods will be developed for the closed-loop online control of the cardiac organoid maturation. The developed hardware and software can be applied to virtually any current biological systems, in which the change of cellular states can be reliably recorded and controlled through the electronic sensors and actuators. The success of this proposal will further merge the field of AI, nanoelectronics, and biology, bringing unlimited opportunities for access and control to biological and biomedical engineering. The multidisciplinary teamwork will represent a successful case that schools of thought from diverse fields including bioengineering, machine learning, statistics, control theory, etc. inspire and complement each other to create state-of-the-art research results in each field. The research team will also collaborate with internal and external partners to launch educational and societal activities for students from diverse backgrounds, such as providing e-seminars, workshops and new courses for undergraduate students on advanced nanoelectronics fabrication, and workshops and tours for local K-12 students to explore stem cell culture, online videos to disseminate new research in genomics, mathematical and computational modeling, integration of AI, nanoelectronics, and biology.We propose to develop a seamless integration of cyber-physical systems with biological systems, enabling a closed-loop control, capable of real-time, bidirectionally, and long-term stably interrogating and intervening cellular activities across the 3D volume of tissue networks at single-cell resolution. As a demonstration, we will apply this cyber-physical-biological system to the hiPSC-CM organoids, promoting and accelerating their maturation. We will achieve our goal through the following 4 technical innovations: (A) developing technologies to integrate stretchable mesh nanoelectronics with multifunctional sensors and actuators to the cardiac organoids, enabling real-time monitoring and control of organoid development; (B) precisely registering electronic sensors during in situ single-cell RNA sequencing to determine the molecular maturation of cardiac organoids and correlate spatial gene expression profiling with sensing data at single-cell resolution; (C) developing novel machine learning models and tools to identify the statistical interference between gene expression and organoid-wide electrical and mechanical recording and also building online predictive models to real-time determine the maturation of cardiac organoids; (D) developing effective and scalable Reinforcement Learning (RL) methods to determine optimized electrical activation patterns to promote the maturation of cardiac organoids and to test its performance in patient-specific cardiac organoids.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

确定和控制人诱导多能干细胞 (hiPSC) 衍生组织成熟的能力对于组织工程、再生医学、药理学和合成生物学至关重要，这需要跨三维空间的细胞活动的询问和干预。 3D）组织体积以及细胞分辨率下组织发育的时间过程该提案旨在建立一个支持人工智能的网络物理生物系统来监测和控制 hiPSC 衍生心肌细胞（hiPSC-CM）的成熟。拟议的研究将开发“类组织”纳米电子学，可以集成到正在发育的心脏类器官中，将电子传感器和执行器网络分布在组织的整个 3D 体积中，并实现组织级记录和控制。单细胞分辨率的整个开发过程将用于将基因表达数据与连续物理传感数据相结合，以解释在线传感数据和网络数据。控制方法将开发的硬件和软件可用于几乎任何当前的生物系统，其中细胞状态的变化可以通过电子传感器和执行器可靠地记录和控制。该提案的成功将进一步融合人工智能、纳米电子学和生物学领域，为生物和生物医学工程带来无限的访问和控制机会，多学科团队合作将成为生物工程、机器等不同领域思想流派的成功案例。学习，统计学、控制理论等相互启发、相互补充，创造出各个领域最先进的研究成果。研究团队还将与内部和外部合作伙伴合作，为来自不同背景的学生开展教育和社会活动，例如为本科生提供有关先进纳米电子制造的电子研讨会、讲习班和新课程，为当地 K-12 学生提供探索干细胞培养的讲习班和参观活动、在线视频以传播基因组学、数学和计算建模、集成方面的新研究人工智能，我们建议开发网络物理系统与生物系统的无缝集成，实现闭环控制，能够实时、双向、长期稳定地询问和干预整个3D体积的细胞活动作为示范，我们将把这个网络物理生物系统应用于 hiPSC-CM 类器官，促进和加速其成熟，我们将通过以下 4 项技术来实现我们的目标。创新：(A) 将可拉伸网状纳米电子器件与多功能传感器和执行器集成到心脏类器官中的技术，从而能够实时监测和控制类器官的发育；(B) 在原位单细胞 RNA 测序过程中精确记录电子传感器以确定心脏类器官的分子成熟，并将空间基因表达谱与单细胞分辨率的传感数据相关联；(C) 开发新的机器学习模型和工具，以识别基因表达与类器官范围内的电和机械记录之间的统计干扰；实时确定心脏类器官成熟的在线预测模型；（D）开发有效且可扩展的强化学习（RL）方法来确定优化的电激活模式，以促进心脏类器官的成熟并测试其在特定患者心脏中的性能该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Model Linkage Selection for Cooperative Learning

合作学习的模型联动选择

• 发表时间: 2021-01
• 期刊: Journal of machine learning research
• 影响因子: 6
• 作者: Zhou, Jiaying;Ding, Jie;Tan, Kean Ming;Tarokh, Vahid
• 通讯作者: Tarokh, Vahid

GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations

GAL：用于分散式多组织协作的梯度辅助学习

• 发表时间: 2021-06-02
• 作者: Enmao Diao;Jie Ding;V. Tarokh
• 通讯作者: V. Tarokh

Zeroth-order feedback optimization for cooperative multi-agent systems

协作多智能体系统的零阶反馈优化

• DOI: 10.1016/j.automatica.2022.110741
• 发表时间: 2023-02
• 期刊: Automatica
• 影响因子: 6.4
• 作者: Tang, Yujie;Ren, Zhaolin;Li, Na
• 通讯作者: Li, Na

Tissue-embedded stretchable nanoelectronics reveal endothelial cell–mediated electrical maturation of human 3D cardiac microtissues

组织嵌入的可拉伸纳米电子学揭示了内皮细胞介导的人类 3D 心脏微组织的电成熟

• DOI: 10.1126/sciadv.ade8513
• 发表时间: 2023-03-10
• 期刊: Science Advances
• 影响因子: 13.6
• 作者: Lin, Zuwan;Garbern, Jessica C.;Liu, Ren;Li, Qiang;Juncosa, Estela Mancheno;Elwell, Hannah L. T.;Sokol, Morgan;Aoyama, Junya;Deumer, Undine-Sophie;Hsiao, Emma;Sheng, Hao;Lee, Richard T.;Liu, Jia
• 通讯作者: Liu, Jia

Score-Based Hypothesis Testing for Unnormalized Models

非标准化模型的基于分数的假设检验

• DOI: 10.1109/access.2022.3187991
• 发表时间: 2022-01
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Wu, Suya;Diao, Enmao;Elkhalil, Khalil;Ding, Jie;Tarokh, Vahid
• 通讯作者: Tarokh, Vahid